It is important to accurately predict internal temperature and humidity conditions for a climate controlled building. Accurate predictions can support optimal operation and evaluation of a heating, ventilation, and air conditioning (HVAC) system, and facilitate the efficient operation of the HVAC system for a changing internal and external climatic environment over a planning time interval.
In a building climate control systems with a HVAC system, a number of control signals are usually applied to the system based on comfort for occupants. Comfort is usually dependent on temperature and humidity. For each day, the HVAC operation plans should keep the temperature and humidity of air in building zones within a certain range under various outside and inside environmental conditions.
There are a number of factors that affect the internal temperature and humidity of buildings. Among these factors, the HVAC system cooling and thermal output and ventilation rates are controllable by a HVAC controller. Some factors are predictable, such as the outside air temperature and humidity. Some factors are controllable, such as HVAC operations. Some factors, such as building thermal characteristics and occupancy pattern, are relatively constant for a specific building, but not accurately measurable because human activity generates extra heat and moisture. All these inputs to the building control system lead to difficulty in an accurate prediction of the internal building temperature and humidity.
Most of known building models use temperature models and humidity models that operate independently. Because temperature and humidity dynamics are usually coupled, the performance of those models is usually suboptimal when temperature and humidity are considered independently.
It is desired to improve the performance of models for building climate control systems.